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  <channel>
    <title>Udo H.</title>
    <link>http://repub.eur.nl/res/aut/29280/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>A generic methodology for developing fuzzy decision models (Article)</title>
      <link>http://repub.eur.nl/res/pub/31950/</link>
      <pubDate>2012-01-01T00:00:00Z</pubDate>
      <description>An important paradigm in decision-making models is utility-maximization where most models do not include actors' motives. Fuzzy set theory on the other hand offers a method to simulate human decision-making. However, the literature describing expert-driven fuzzy logic models, rarely gives precise details on the methodology (to be) used. To fill the gap, this paper describes a methodology of 10 steps to model individual actor's drivers, motives, hereby taking into account the ecological, social and economic context. Testing the methodology on the composition of mixed farming systems in the Mekong Delta, Vietnam, showed that manual model development is not a waterfall approach but requires feedback loops, except for model implementation. Using feed-back loops, the proposed 10 step method allowed to include human drivers and motives other than utility-maximization and to maintain a degree of transparency hard to achieve when using automated procedures. </description>
    </item> <item>
      <title>Using fuzzy logic modelling to simulate farmers' decision-making on diversification and integration in the Mekong Delta, Vietnam (Article)</title>
      <link>http://repub.eur.nl/res/pub/23845/</link>
      <pubDate>2011-03-01T00:00:00Z</pubDate>
      <description>To reveal farmers' motives for on-farm diversification and integration of farming components in the Mekong Delta, Vietnam, we developed a fuzzy logic model (FLM) using a 10-step approach. Farmers' decision-making was mimicked in a three-layer hierarchical architecture of fuzzy inference systems, using data of 72 farms. The model includes three variables for family motives of diversification, six variables related to component integration, next to variables for the production factors and for farmers' appreciation of market prices and know-how on 10 components. To obtain a good classification rate of the less frequent activities, additional individual fine-tuning was necessary after general model calibration. To obtain the desired degree of sensitivity to each variable, it was necessary to use up to five linguistic values for some of the input and output variables in the intermediate hierarchical layers. Model's sensitivity to motivational variables determining diversification and integration was of the same magnitude as its sensitivity to market prices and farmers' know-how of the activities, but less than its sensitivity to labour, capital and land endowment. Modelling to support strategic decision-making seems too elaborate for individual farms, but FLM will be useful to integrate farmers' opinions in strategic decision-making at higher hierarchical levels. </description>
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